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Top 20 MLOps Case Studies & Success Stories in 2024

Cem Dilmegani
Updated on Jan 12
3 min read

Organizations have started to adopt MLOps practices to standardize and streamline their ML development and operationalization processes. But the journey is not easy and there is much to learn. 

We’ve compiled 20 MLOps success stories and case studies to help businesses that are looking to improve their ML processes.

Introducing MLOps to your business

To implement MLOps practices in your business, you need to have a supporting infrastructure. You can either build this infrastructure with your internal resources, or buy an MLOps solution that provides the necessary infrastructure. We will cover both approaches below.

In-house MLOps infrastructure

As we discussed in our previous article, building AI capabilities with internal resources can demand extensive time, effort, and budget. We suggested that most small, and non-tech, companies should work with AI vendors instead of building in-house solutions.

This also applies to MLOps infrastructure. Building a functioning and scalable infrastructure can take over a year and requires hiring additional data scientists, ML engineers, DevOps professionals, etc.

Large companies, like Uber or Facebook, have the resources and the data to afford such investments. However, most companies do not have these resources. More importantly, most of them don’t need such investments because there are capable AI and ML solutions that can easily meet their needs.

Buying an MLOps solution

The other option is buying MLOps solutions that provide the necessary infrastructure to implement MLOps practices in your business. There are tools that cover a subset of MLOps tasks such as:

  • Data management
  • Modeling
  • Operationalization

These tools can be integrated with other solutions which can help you to create an ML pipeline. There are also MLOps platforms that provide end-to-end machine learning lifecycle management. You can explore both types of tools in our in-depth article on MLOps tools.

Aside from the customization opportunities that come with building an in-house MLOps solution, these off-the-shelf MLOps tools can meet the needs of most businesses with rapid deployment at a fraction of the cost.

MLOps case studies

Below is a list of MLOps examples and case studies that we’ve compiled from different vendors and resources:

AgroScoutClearMLUnited StatesAgriculture
-Increased data volume 100x without growing the data team -Increased experiment volume 50x -Decreased the time to production by 50%*NetherlandsE-Commerce-Ability to scale AI with 150 customer facing ML models
CollectiveCrunchValohaiFinlandIT-Reduced the model development time by a factor of five
-Reduce the time for reproducing experiments by 50% -Twice as much ML work handled without additional staff -Projected savings of $1.3 million over the next year
EcolabIguazioUnited StatesChemicalsDecreased model deployment times from 12 months to 30-90 days
-Running 10X the number of experiments with the same amount of effort by automated machine orchestration and experiment tracking
LevityValohaiGermanyIT-Time and resource savings after failed in-house MLOps projects
NetAppIguazioUnited StatesIT
-Improved the time to develop and deploy new AI services by 6-12x -Reduced operating costs by 50%
Neural GuardClearMLUnited KingdomAviation
-Saving on cost and shortening time-to-market -Ongoing saving related to not hiring additional staff
NTUC IncomeDataRobotSingaporeInsurance
-Reduced the time to generate results from a few days to less than an hour
Oyak CementDataRobotTurkeyManufacturing
-Increased alternative fuel usage by 7 times -Cut 2% of total CO2 emissions -Reduced costs by $39 million
PayoneerIguazioUnited StatesFinancial Services
-Built a scalable and reliable fraud prediction and prevention model that analyzes fresh data in real-time and adapts to new threats
PhilipsClearMLNetherlandsHealthcare-Hours saved through streamlined experiment tracking and automatic documentation
-Simplified ML development workflow to create AI applications at scale an, in real time
Sharper ShapeValohaiUnited StatesIT
-Automation of infrastructure and experiment management tasks that takes a third of data scientists time -New data scientists can be onboarded in a quarter of the time
Steward Health CareDataRobotUnited StatesHealthcare
-$2 million/year in savings from nurses hours paid per patient day -$10 million/year savings from reducing patient length of stay
The Adecco GroupDataRobotSwitzerlandHR
-37% reduction in the number of CVs reviewed 10% productivity gain -Launched 60 projects with 3000 models
TheatorClearMLUnited StatesHealthcare-$130K-$170K annual savings directly related to MLOps
-Streamlined ML workflow with simple experiment tracking, feature store, and documentation
Uber*United StatesTransportation
-Developed their own ML platform Michelangelo -From zero to hundreds of ML products in three years thanks to MLOps practices

*Companies that build their own MLOps infrastructure

If you need a tool to implement MLOps practices in your business, don’t forget to check our sortable/filterable list of MLOps platforms.

And if you found yourself having more questions, feel free to ask:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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